Draft amendments to the standard external trade statistics classification of East Caribbean Common Market Countries

Includes bibliography

Classification of Time-Use Activities for Latin America and the Caribbean (CAUTAL)

The Classification of Time-Use Activities for Latin America and the Caribbean (CAUTAL) is the outcome of an extensive working process undertaken by the Working Group on Gender Statistics of the Statistical Conference of the Americas (SCA) to meet the need of Latin American and Caribbean countries for a gender-sensitive instrument appropriate to the regional context that could be used to harmonize and standardize time-use surveys and produce statistics in this area.

Classification of honeys from Pará State (Amazon region, Brazil) produced by three different species of bees

Vinte e sete amostras de mel, produzidas em dez cidades do Estado do Pará (Região Amazônica, norte do Brasil) por três espécies diferentes de abelhas (Apis mellifera, Melipona fasciculata e Melipona flavoneata), foram analisadas em seus teores de elementos minerais (Al, As, Ba, Be, Bi, Ca, Cd, Co, Cr, Cu, Fe, K, Li, Mg, Mn, Na, Ni, Sr e Zn) e alguns parâmetros fisicoquímicos (cor, umidade, densidade, pH, sólidos insolúveis e solúveis totais, cinzas, condutividade elétrica, índice de formol, acidez livre, hidroximetilfurfural, açúcares redutores e totais e sacarose). Os teores minerais foram determinados via espectrometria de emissão atômica por plasma acoplado indutivamente (ICP OES) e as análises dos parâmetros físico-químicos seguiram metodologias oficiais. Os resultados das análises físico-químicas apresentaram-se de acordo com a legislação nacional e internacional, bem como com outros trabalhos similares ao redor do mundo. A análise estatística multivariada (análise por agrupamento hierárquico (HCA) e por componentes principais (PCA)) foi aplicada aos resultados dos teores metálicos e aos parâmetros físico-químicos, sendo possível a separação das amostras de mel conforme a espécie produtora.

Geostatistics and remote sensing methods in the classification of images of areas cultivated with citrus

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Development of a CAD system for automatic classification of microcalcifications based on FPGA

This paper presents a Computer Aided Diagnosis (CAD) system that automatically classifies microcalcifications detected on digital mammograms into one of the five types proposed by Michele Le Gal, a classification scheme that allows radiologists to determine whether a breast tumor is malignant or not without the need for surgeries. The developed system uses a combination of wavelets and Artificial Neural Networks (ANN) and is executed on an Altera DE2-115 Development Kit, a kit containing a Field-Programmable Gate Array (FPGA) that allows the system to be smaller, cheaper and more energy efficient. Results have shown that the system was able to correctly classify 96.67% of test samples, which can be used as a second opinion by radiologists in breast cancer early diagnosis. (C) 2013 The Authors. Published by Elsevier B.V.

Classification of Eucalyptus urograndis hybrids under different water availability based on biometric traits

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Automatic classification of prostate stromal tissue in histological images using Haralick descriptors and Local Binary Patterns

In this paper we presente a classification system that uses a combination of texture features from stromal regions: Haralick features and Local Binary Patterns (LBP) in wavelet domain. The system has five steps for classification of the tissues. First, the stromal regions were detected and extracted using segmentation techniques based on thresholding and RGB colour space. Second, the Wavelet decomposition was applied in the extracted regions to obtain the Wavelet coefficients. Third, the Haralick and LBP features were extracted from the coefficients. Fourth, relevant features were selected using the ANOVA statistical method. The classication (fifth step) was performed with Radial Basis Function (RBF) networks. The system was tested in 105 prostate images, which were divided into three groups of 35 images: normal, hyperplastic and cancerous. The system performance was evaluated using the area under the ROC curve and resulted in 0.98 for normal versus cancer, 0.95 for hyperplasia versus cancer and 0.96 for normal versus hyperplasia. Our results suggest that texture features can be used as discriminators for stromal tissues prostate images. Furthermore, the system was effective to classify prostate images, specially the hyperplastic class which is the most difficult type in diagnosis and prognosis.

Local classification of singular hexagonal 3-webs with holomorphic Chern connection form and infinitesimal symmetries

Implicit ODE, cubic in derivative, generically has no infinitesimal symmetries even at regular points with distinct roots. Cartan showed that at regular points, ODEs with hexagonal 3-web of solutions have symmetry algebras of the maximal possible dimension 3. At singular points such a web can lose all its symmetries. In this paper we study hexagonal 3-webs having at least one infinitesimal symmetry at singular points. In particular, we establish sufficient conditions for the existence of non-trivial symmetries and show that under natural assumptions such a symmetry is semi-simple, i.e. is a scaling in some coordinates. Using the obtained results, we provide a complete classification of hexagonal singular 3-web germs in the complex plane, satisfying the following two conditions: 1) the Chern connection form is holomorphic at the singular point, 2) the web admits at least one infinitesimal symmetry at this point. As a by-product, a classification of hexagonal weighted homogeneous 3-webs is obtained.

Classification of intact fresh plums according to sweetness using time-domain nuclear magnetic resonance and chemometrics

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Land cover classification of Lago Grande de Curuai floodplain (Amazon, Brazil) using multi-sensor and image fusion techniques

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Influence of genotype-environment interaction on the classification of Nellore bulls in Southern Brazil

The aim of this study was to assess the occurrence of genotype-environment interaction, as well as its effects on the magnitude of genetic parameters and the classification of Nellore breeding bulls for the trait adjusted weight at 205 days (W205) on Southern Brazil. The components of (co)variance were estimated by Bayesian inference, using a linear-linear animal model in a bi-trait analysis. The proposed model for the analyses considers as random the direct additive genetic and maternal effects and residual effects, and as fixed effects the contemporary groups, sex, season of birth and weighing, and calving age as covariable (linear and quadratic effects). The a posteriori mean estimates of the direct heritabilities for W205 in the three States varied from 0.24 in Paraná (PR) to 0.34 in Santa Catarina (SC). The estimates of maternal heritability varied from 0.23 in SC and Rio Grande do Sul (RS) to 0.28 in PR. The a posteriori mean distributions of the genetic correlation varied from 0.52 between SC and RS, to 0.84 between PR and RS, suggesting that the best breeding bulls in SC are not the same as in RS.

Classification of histological images based on the stationary wavelet transform

Non-Hodgkin lymphomas are of many distinct types, and different classification systems make it difficult to diagnose them correctly. Many of these systems classify lymphomas only based on what they look like under a microscope. In 2008 the World Health Organisation (WHO) introduced the most recent system, which also considers the chromosome features of the lymphoma cells and the presence of certain proteins on their surface. The WHO system is the one that we apply in this work. Herewith we present an automatic method to classify histological images of three types of non-Hodgkin lymphoma. Our method is based on the Stationary Wavelet Transform (SWT), and it consists of three steps: 1) extracting sub-bands from the histological image through SWT, 2) applying Analysis of Variance (ANOVA) to clean noise and select the most relevant information, 3) classifying it by the Support Vector Machine (SVM) algorithm. The kernel types Linear, RBF and Polynomial were evaluated with our method applied to 210 images of lymphoma from the National Institute on Aging. We concluded that the following combination led to the most relevant results: detail sub-band, ANOVA and SVM with Linear and RBF kernels.

Classification of intact açai (Euterpe oleracea Mart.) and jucara (Euterpe edulis Mart) fruits based on dry matter content by means of near infrared spectroscopy

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)